Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications

Speech signal processing and its recognition system have gained a lot of attention from last few years due to its widespread application. In this paper, a novel approach is proposed for diagnosis and monitoring the Parkinson’s Disease (PD) which is the second most severe neurological disease in the world. PD is a neurodegenerative disease which impairs person’s balance, motor skills, speech, and other characteristics such as decision making process, emotions, and sensation. Here, we proposed a cloud based framework for detecting and monitoring Parkinson patients that will enable healthcare service in low resource setting. In the developing countries, where most of the people do not get proper healthcare services and are not well aware of Parkinson’s disease, let alone detecting and getting healthcare for PD, this system can be very practical and useful. For this system, the patients of rural areas, patients from the regions where doctors are not available, can communicate to the doctors only if they have internet connections in their smart phones to access the cloud. Doctors can check and detect patient’s PD by checking their voice disorders or Dysphonia over cloud. With this system, a PD patient can be easily detected and diagnosed by giving their voice samples through their phones, regardless of their location. Based on the evaluation, our proposed systems are avail to achieve 96.6% accuracy in the cloud environment for detecting PD. It is expected that the proposed framework will have great potential to enable healthcare service for PD patients, who live in remote areas, especially in developing countries.     

A human–computer interaction approach for healthcare

Universal Access in the Information Society -     

SmartHerd management: A microservices-based fog computing–assisted IoT platform towards data-driven smart dairy farming

Internet of Things (IoT), fog computing, cloud computing, and data-driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing–assisted end-to-end IoT platform for animal behavior analysis and health monitoring in a dairy farming scenario. The platform follows a microservices-oriented design to assist the distributed computing paradigm and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6-month mature real-world deployment, wherein the data from wearables on cows is sent to a fog-based platform for data classification and analysis, which includes decision-making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog-based computational assistance in the SmartHerd setup, we see an 84% reduction in amount of data transferred to the cloud as compared with the conventional cloud-based approach.     

A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability

Engineering with Computers - Stability of the soil slopes is one of the most challenging issues in civil engineering projects. Due to the complexity and non-linearity of this threat, utilizing...     

Pods – A novel intelligent energy efficient and dynamic frequency scalings for multi-core embedded architectures in an IoT environment

In the Advent of the Internet of Things (IoT), embedded architecture takes an important dimension in terms of energy and accomplishment. The embedded system needs more and more intelligent algorithms for better performance and energy efficiency to fit into an IoT scenario. Moreover, with the existence of high-performance multi-core embedded architectures, achievements of energy efficiency remains in the dark side of the research. Several algorithms such as dynamic frequency scaling, thread mapping, starvation methodologies were proposed in embedded architectures for efficient usages of clock frequencies and these features were used as the energy saving modes in which the consumption of energy in the embedded architectures are being controlled. But these methods have several backlogs which permits the use of consumption in the embedded architectures. Considering the above features, this paper proposes a new methodology PODS(Predictors for Optimized Dynamic Scaling) which integrates a powerful machine learning algorithm for scaling the clock frequencies by the input workloads and allocation of the core depending based on the workload. The proposed framework PODS has different phases of working namely workload extraction, characterization, and optimization using BAT algorithms and prediction extreme Machine - Learning. The algorithm was tested on ARM/Cortex architectures (Raspberry Pi 3 Model B+), an evaluated algorithm using the IoMT benchmarks and various parameters that include energy consumption, accuracy of detection/prediction was determined and analyzed. It is found that the implementation of the proposed framework in the test is seen resulting between 35 and 40% reduction in the consumption of the power.     

A novel complex-system-view-based method for system effectiveness analysis: Monotonic indexes space

1 Introduction System effectiveness is the extent to which system meet requirements. It is a difficult and important problem in the social and natural science, therefore, many methods for system effectiveness have been proposed by scientists. They are as …     

A critical review on idealization and modeling for interaction among soil–foundation–structure system

The interaction among structures, their foundations and the soil medium below the foundations alter the actual behaviour of the structure considerably than what is obtained from the consideration of the structure alone. Thus, a reasonably accurate model for the soil–foundation–structure interaction system with computational validity, efficiency and accuracy is needed in improved design of important structures. The present study makes an attempt to gather the possible alternative models available in the literature for this purpose. Emphasis has been given on the physical modeling of the soil media, since it appears that the modeling of the structure is rather straightforward. The strengths and limitations of the models described in a single paper may be of help to the civil engineers to choose a suitable one for their study and design.     

A novel hybrid BPSO–SCA approach for feature selection

Natural Computing - Nature is a great source of inspiration for solving complex problems in real-world. In this paper, a hybrid nature-inspired algorithm is proposed for feature selection problem....     

A novel hybrid PSO–GWO algorithm for optimization problems

Engineering with Computers - In this study, we propose a new hybrid algorithm fusing the exploitation ability of the particle swarm optimization (PSO) with the exploration ability of the grey wolf...     

A novel hybrid CNN–SVM classifier for recognizing handwritten digits

This paper presents a hybrid model of integrating the synergy of two superior classifiers: Convolutional Neural Network (CNN) and Support Vector Machine (SVM), which have proven results in recognizing different types of patterns. In this model, CNN works as a trainable feature extractor and SVM performs as a recognizer. This hybrid model automatically extracts features from the raw images and generates the predictions. Experiments have been conducted on the well-known MNIST digit database. Comparisons with other studies on the same database indicate that this fusion has achieved better results: a recognition rate of 99.81% without rejection, and a recognition rate of 94.40% with 5.60% rejection. These performances have been analyzed with reference to those by human subjects.     

A novel hybrid PSO–GWO approach for unit commitment problem

Particle swarm optimization algorithm is a inhabitant-based stochastic search procedure, which provides a populace-based search practice for getting the best solution from the problem by taking particles and moving them around in the search space and efficient for global search. Grey Wolf Optimizer is a recently developed meta-heuristic search algorithm inspired by Canis-lupus. This research paper presents solution to single-area unit commitment problem for 14-bus system, 30-bus system and 10-generating unit model using swarm-intelligence-based particle swarm optimization algorithm and a hybrid PSO–GWO algorithm. The effectiveness of proposed algorithms is compared with classical PSO, PSOLR, HPSO, hybrid PSOSQP, MPSO, IBPSO, LCA–PSO and various other evolutionary algorithms, and it is found that performance of NPSO is faster than classical PSO. However, generation cost of hybrid PSO–GWO is better than classical and novel PSO, but convergence of hybrid PSO–GWO is much slower than NPSO due to sequential computation of PSO and GWO.     

CPAS: the UK’s national machine learning-based hospital capacity planning system for COVID-19

Machine Learning - The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing...     

Thermodynamic assessment and database for the Cu–Fe–O–S system

The previously obtained thermodynamic databases for the Cu–Fe–S, Cu–Fe–O, Fe–O–S and Cu–O–S ternary systems have been combined and used to predict thermodynamic equilibria in the quaternary Cu–Fe–O–S system. The available experimental data were compared with model predictions. Minor modifications of model parameters were required to better describe the experimental points in the quaternary system; the effect of these changes was verified in the ternary subsystems. The procedure was developed to calculate the isothermal sections of the phase diagram of the quaternary system inside the tetrahedron. The liquid phase over the whole composition range from metallic liquid to sulfide melt to oxide melt has been described by a single model developed within the framework of the quasichemical formalism. The obtained self-consistent set of model parameters can be used as a basis for the development of a thermodynamic database for simulation of copper smelting and converting.     

A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis

The Journal of Supercomputing - As the number of users getting acquainted with the Internet is escalating rapidly, there is more user-generated content on the web. Comprehending hidden opinions,...     

A novel minorization–maximization framework for simultaneous feature selection and clustering of high-dimensional count data

Pattern Analysis and Applications - Count data are commonly exploited in machine learning and computer vision applications; however, they often suffer from the well-known curse of dimensionality,...     

A generation alternation model for user–system cooperative evolutionary computation

User?Csystem cooperative evolution (CEUS) is an evolutionary computation (EC) method to optimize quantitative and qualitative criteria. In previous work of CEUS, the whole population update is performed at every generation, and the user observes very few individuals. This paper proposes a generation alternation model designed for CEUS. The proposed model allows a user to find widely varied individuals in addition to the best individuals by replacing just one individual in a population for each generation, and consequently, contributes user??s idea generation by enhancing divergent thinking.     

A novel hybrid DE–random search approach for unit commitment problem

Differential evolution (DE) is a population-based stochastic search algorithm, whose simple yet powerful and straightforward features make it very attractive for numerical optimization. DE uses a rather greedy and less stochastic approach to problem-solving than other evolutionary algorithms. DE combines simple arithmetic operators with the classical operators of recombination, mutation and selection to evolve from a randomly generated starting population to a final solution. Although global exploration ability of DE algorithm is adequate, its local exploitation ability is feeble and convergence velocity is too low and it suffers from the problem of untime convergence for multimodal objective function, in which search process may be trapped in local optima and it loses its diversity. Also, it suffers from the stagnation problem, where the search process may infrequently stop proceeding toward the global optimum even though the population has not converged to a local optimum or any other point. To improve the exploitation ability and global performance of DE algorithm, a novel and hybrid version of DE algorithm is presented in the proposed research. This research paper presents a hybrid version of DE algorithm combined with random search for the solution of single-area unit commitment problem. The hybrid DE–random search algorithm is tested with IEEE benchmark systems consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is compared with other well-known evolutionary, heuristics and meta-heuristics search algorithms, and by experimental analysis, it has been found that proposed algorithm yields global results for the solution of unit commitment problem.

     

A novel binary gaining–sharing knowledge-based optimization algorithm for feature selection

Neural Computing and Applications - To obtain the optimal set of features in feature selection problems is the most challenging and prominent problem in machine learning. Very few human-related...     

Thermodynamic re-assessment and experimental confirmation for the Zn–Mn system

The Zn–Mn system has been critically re-assessed based on available experimental data from literature by CALPHAD (CALculation of PHAse Diagram) approach. Solution phases, viz. liquid, (δMn), (γMn), (βMn), (αMn) and (Zn) were described using substitutional solution model. Besides, the ε, αˊ, γ, δ, δ₁ and ζ phases were described by the sub-lattice models based on the reported crystallographic information and homogeneity ranges. The β₁ phase was treated as a stoichiometric compound since no distinct homogeneity range of this phase was reported. Thermodynamic parameters on the whole system compatible with the databases of Thermo-Calc and Pandat were first obtained. Compared with previous assessments, calculations using the presently obtained thermodynamic parameters achieved a significant improvement. To further verify the reliability of the obtained thermodynamic parameters, eight as-cast Zn–Mn alloys were prepared and the phase compositions and solidified microstructures were analyzed by X-ray diffraction (XRD) and electron probe microanalysis (EPMA). It was shown that the observed experimental results agreed well with the Scheil solidification calculations.